Double-time analysis of oil rig activity

ABSTRACT

A method and apparatus for collection and analysis of oil rig activity is described. The method includes collecting wellsite data from a plurality of sources, including real-time data sources and macroscopic reports. In certain embodiments, the collected data may be standardized based on templates specifying data format and presentation. Additionally, the data may be automatically corrected by using data error lists that identify algorithms for diagnosing and correcting errors in the data. Data collected from multiple sources may be time-aligned so that data from different sources may be correlated together by time. In certain embodiments, time aligning the data may include adjusting manually-logged timestamps for events in macroscopic reports based on real-time data. In this way, heterogeneous data from a plurality of sources may be homogenized. Optionally, the homogenized data may be used as inputs for wellsite data analysis or to produce various types of quality reports.

BACKGROUND

The present disclosure relates generally to oil field exploration and,more particularly, to a system and method for analysis of oil rigactivity.

The measurement of various types of data during oil rig activities iswell known in the subterranean well drilling and completion art.Real-time data is generated from, for example, wellsite sensors,measurement-while-drilling/logging-while-drilling tools, and softwareapplication logs. Additionally, macroscopic reports may be generatedthat record wellsite metadata (e.g., type of drillbit, casinginformation, etc.) and data reflecting wellsite operations during flattime (i.e., time during which real-time sensors are not changing).Macroscopic reports may also include the wellsite operators' manual logsof operations.

The analysis of the various data is well known in the art. However, suchdata analysis and reporting may be difficult and time consuming whendata may be fragmented across different data sources, unstandardized,and/or contain errors. Data analysis must usually be performed eitherentirely manually or with substantial manual oversight. Further, it maybe difficult to combine or compare data from multiple wellsites, orbetween wellsites overseen by different operating companies, due to thedifferent methods and formats for data gathering and recording.

FIGURES

Some specific exemplary embodiments of the disclosure may be understoodby referring, in part, to the following description and the accompanyingdrawings.

FIG. 1 is a diagram showing an illustrative logging while drillingenvironment, according to aspects of the present disclosure.

FIG. 2 is a diagram showing an illustrative wireline loggingenvironment, according to aspects of the present disclosure.

FIG. 3 is a diagram of an example information handling system, accordingto aspects of the present disclosure

FIG. 4 is a flowchart showing an overview of the steps for analyzing oilrig activity, according to the present disclosure.

FIG. 5 is a flowchart showing one embodiment of steps for aggregatingand pre-processing data, according to the present disclosure.

FIG. 6 is a flowchart showing one embodiment of steps for automaticallycollecting wellsite data, according to the present disclosure.

FIG. 7 is a flowchart showing one embodiment of steps for standardizingcollected wellsite data, according to the present disclosure.

FIG. 8 is a flowchart showing one embodiment of steps for identifyingand correcting errors in wellsite data, according to the presentdisclosure.

FIG. 9 is a flowchart showing one embodiment of steps for documentingdata issues and alterations in a receipt file, according to the presentdisclosure.

FIG. 10 is a flowchart showing one embodiment of steps for preparing aQA/QC cover report from wellsite data, according to the presentdisclosure.

FIG. 11 is a flowchart showing one embodiment of steps for macroscopicreport interpretation, according to the present disclosure.

FIG. 12 is a flowchart showing one embodiment of steps for templatemapping a macroscopic report, according to the present disclosure.

FIG. 13 is a flowchart showing one embodiment of steps for templatemapping a macroscopic report, according to the present disclosure.

FIG. 14 is a flowchart showing one embodiment of steps for time-aligningdata and verifying interpretation, according to the present disclosure.

FIG. 15 is a flowchart showing one embodiment of steps for following anon-linear process map, according to the present disclosure.

FIG. 16 is a flowchart showing one embodiment of steps for data analysisand reporting, according to the present disclosure.

While embodiments of this disclosure have been depicted and describedand are defined by reference to exemplary embodiments of the disclosure,such references do not imply a limitation on the disclosure, and no suchlimitation is to be inferred. The subject matter disclosed is capable ofconsiderable modification, alteration, and equivalents in form andfunction, as will occur to those skilled in the pertinent art and havingthe benefit of this disclosure. The depicted and described embodimentsof this disclosure are examples only, and not exhaustive of the scope ofthe disclosure.

DETAILED DESCRIPTION

For purposes of this disclosure, an information handling system mayinclude any instrumentality or aggregate of instrumentalities operableto compute, classify, process, transmit, receive, retrieve, originate,switch, store, display, manifest, detect, record, reproduce, handle, orutilize any form of information, intelligence, or data for business,scientific, control, or other purposes. For example, an informationhandling system may be a personal computer, a network storage device, orany other suitable device and may vary in size, shape, performance,functionality, and price. The information handling system may includerandom access memory (RAM), one or more processing resources such as acentral processing unit (CPU) or hardware or software control logic,ROM, and/or other types of nonvolatile memory. Additional components ofthe information handling system may include one or more disk drives, oneor more network ports for communication with external devices as well asvarious input and output (I/O) devices, such as a keyboard, a mouse, anda video display. The information handling system may also include one ormore buses operable to transmit communications between the varioushardware components. It may also include one or more interface unitscapable of transmitting one or more signals to a controller, actuator,or like device.

For the purposes of this disclosure, computer-readable media may includeany instrumentality or aggregation of instrumentalities that may retaindata and/or instructions for a period of time. Computer-readable mediamay include, for example, without limitation, storage media such as adirect access storage device (e.g., a hard disk drive or floppy diskdrive), a sequential access storage device (e.g., a tape disk drive),compact disk, CD-ROM, DVD, RAM, ROM, electrically erasable programmableread-only memory (EEPROM), solid state drives, and/or flash memory; aswell as communications media such wires, optical fibers, microwaves,radio waves, and other electromagnetic and/or optical carriers; and/orany combination of the foregoing.

Illustrative embodiments of the present disclosure are described indetail herein. In the interest of clarity, not all features of an actualimplementation may be described in this specification. It will of coursebe appreciated that in the development of any such actual embodiment,numerous implementation-specific decisions are made to achieve thespecific implementation goals, which will vary from one implementationto another. Moreover, it will be appreciated that such a developmenteffort might be complex and time-consuming, but would, nevertheless, bea routine undertaking for those of ordinary skill in the art having thebenefit of the present disclosure.

To facilitate a better understanding of the present disclosure, thefollowing examples of certain embodiments are given. In no way shouldthe following examples be read to limit, or define, the scope of theinvention. Embodiments of the present disclosure may be applicable tohorizontal, vertical, deviated, or otherwise nonlinear wellbores in anytype of subterranean formation. Embodiments may be applicable toinjection wells as well as production wells, including hydrocarbonwells. Embodiments may be implemented using a tool that is made suitablefor testing, retrieval and sampling along sections of the formation.Embodiments may be implemented with tools that, for example, may beconveyed through a flow passage in tubular string or using a wireline,slickline, coiled tubing, downhole robot or the like.“Measurement-while-drilling” (“MWD”) is the term generally used formeasuring conditions downhole concerning the movement and location ofthe drilling assembly while the drilling continues.“Logging-while-drilling” (“LWD”) is the term generally used for similartechniques that concentrate more on formation parameter measurement.Devices and methods in accordance with certain embodiments may be usedin one or more of wireline (including wireline, slickline, and coiledtubing), downhole robot, MWD, and LWD operations.

The terms “couple” or “couples” as used herein are intended to meaneither an indirect or a direct connection. Thus, if a first devicecouples to a second device, that connection may be through a directconnection or through an indirect mechanical or electrical connectionvia other devices and connections. Similarly, the term “communicativelycoupled” as used herein is intended to mean either a direct or anindirect communication connection. Such connection may be a wired orwireless connection such as, for example, Ethernet or LAN. Such wiredand wireless connections are well known to those of ordinary skill inthe art and will therefore not be discussed in detail herein. Thus, if afirst device communicatively couples to a second device, that connectionmay be through a direct connection, or through an indirect communicationconnection via other devices and connections.

FIG. 1 is a diagram of a subterranean drilling system 100, according toaspects of the present disclosure. The drilling system 100 comprises adrilling platform 2 positioned at the surface 102. In the embodimentshown, the surface 102 comprises the top of a formation containing oneor more rock strata or layers 18, and the drilling platform 2 may be incontact with the surface 102. In other embodiments, such as in anoff-shore drilling operation, the surface 102 may be separated from thedrilling platform 2 by a volume of water.

The drilling system 100 comprises a derrick 4 supported by the drillingplatform 2 and having a traveling block 6 for raising and lowering adrill string 8. A kelly 10 may support the drill string 8 as it islowered through a rotary table 12. A drill bit 14 may be coupled to thedrill string 8 and driven by a downhole motor and/or rotation of thedrill string 8 by the rotary table 12. As bit 14 rotates, it creates aborehole 16 that passes through one or more rock strata or layers 18. Apump 20 may circulate drilling fluid through a feed pipe 22 to kelly 10,downhole through the interior of drill string 8, through orifices indrill bit 14, back to the surface via the annulus around drill string 8,and into a retention pit 24. The drilling fluid transports cuttings fromthe borehole 16 into the pit 24 and aids in maintaining integrity or theborehole 16.

The drilling system 100 may comprise a bottom hole assembly (BHA)coupled to the drill string 8 near the drill bit 14. The BHA maycomprise a LWD/MWD tool 26 and a telemetry element 28. In certainembodiments, the LWD/MWD tool 26 may be integrated at any point alongthe drill string 8. The LWD/MWD tool 26 may include receivers and/ortransmitters (e.g., wired pipe, antennas capable of receiving and/ortransmitting one or more electromagnetic signals). In some embodiments,the LWD/MWD tool 26 may include a transceiver array that functions asboth a transmitter and a receiver. As the bit extends the borehole 16through the formations 18, the LWD/MWD tool 26 may collect measurementsrelating to various formation properties as well as the tool orientationand position and various other drilling conditions. The orientationmeasurements may be performed using an azimuthal orientation indicator,which may include magnetometers, inclinometers, hall effect sensors,and/or accelerometers, though other sensor types such as gyroscopes maybe used in some embodiments. In embodiments including an azimuthalorientation indicator, resistivity and/or dielectric constantmeasurements may be associated with a particular azimuthal orientation(e.g., by azimuthal binning). The telemetry sub 28 may transfermeasurements from the LWD/MWD tool 26 to a surface receiver 30 and/or toreceive commands from the surface receiver 30. Measurements taken at theLWD/MWD tool 26 may also be stored within the tool 26 for laterretrieval when the LWD/MWD tool 26 is removed from the borehole 16.

In certain embodiments, the drilling system 100 may comprise aninformation handling system 32 positioned at the surface 102. Theinformation handling system 32 may be communicably coupled to thesurface receiver 30 and may receive measurements from the LWD/MWD tool26 and/or transmit commands to the LWD/MWD tool 26 though the surfacereceiver 30. The information handling system 32 may also receivemeasurements from the LWD/MWD tool 26 when it is retrieved at thesurface 102. In certain embodiments, the information handling system 32may process the measurements to determine certain characteristics of theformation 104 (e.g., resistivity, permeability, conductivity, porosity,etc.) In some cases, the measurements and formation characteristics maybe plotted, charted, or otherwise visualized at the information handlingsystem 32 to allow drilling operators to alter the operation of thedrilling system 100 to account for downhole conditions.

At various times during the drilling process, the drill string 8 may beremoved from the borehole 16 as shown in FIG. 2. Once the drill string 8has been removed, measurement/logging operations can be conducted usinga wireline tool 34, i.e., an instrument that is suspended into theborehole 16 by a cable 15 having conductors for transporting power tothe tool and telemetry from the tool body to the surface 102. Thewireline tool 34 may include one or more logging/measurement tools 36having transmitters, receivers, and/or transceivers similar to thosedescribed above in relation to the LWD/MWD tool 26. Thelogging/measurement tool 36 may be communicatively coupled to the cable15. A logging facility 44 (shown in FIG. 1 as a truck, although it maybe any other structure) may collect measurements from the logging tool36, and may include computing facilities (including, e.g., aninformation handling system) for controlling, processing, storing,and/or visualizing the measurements gathered by the logging tool 36. Thecomputing facilities may be communicatively coupled to thelogging/measurement tool 36 by way of the cable 15. In certainembodiments, the information handling system 32 may serve as thecomputing facilities of the logging facility 44.

FIG. 3 is a block diagram showing an example information handling system300, according to aspects of the present disclosure. Informationhandling system 300 may be used with the drilling system described aboveand with other subterranean drilling systems. In certain embodiments,some or all of the steps shown in FIGS. 4-16 and discussed below may beperformed by one or more information handling systems 300.

The information handling system 300 may comprise a processor or CPU 301that is communicatively coupled to a memory controller hub or northbridge 302. Memory controller hub 302 may include a memory controllerfor directing information to or from various system memory componentswithin the information handling system, such as RAM 303, storage element306, and hard drive 307. The memory controller hub 302 may be coupled toRAM 303 and a graphics processing unit 304. Memory controller hub 302may also be coupled to an I/O controller hub or south bridge 305. I/Ohub 305 is coupled to storage elements of the computer system, includinga storage element 306, which may comprise a flash ROM that includes abasic input/output system (BIOS) of the computer system. I/O hub 305 isalso coupled to the hard drive 307 of the computer system. I/O hub 305may also be coupled to a Super I/O chip 308, which is itself coupled toseveral of the I/O ports of the computer system, including keyboard 309and mouse 310. The information handling system 300 further may becommunicably coupled to one or more elements of a drilling system thoughthe chip 308 as well as a visualization mechanism, such as a computermonitor or display.

The information handling systems described above may include softwarecomponents that process and characterize data and software componentsthat generate visualizations from the processed data. As used herein,software or software components may comprise a set of instructionsstored within a computer readable medium that, when executed by aprocessor coupled to the computer readable medium, cause the processorto perform certain actions. In the case of a datacharacterization/processing component, the set of instructions may causethe processor to receive “raw” data from a data source (e.g.,measurements from a LWD/MWD tool), and to process the “raw” usingvarious algorithms or other processing techniques that would beappreciated by one of ordinary skill in the art in view of thisdisclosure and the purposes to be achieved by the data processing. Inthe case of a software component that generates visualizations, the setof instructions may cause the processor to receive processed data from adata characterization/processing component and generate a visualization(e.g, chart, graph, plot, 3-D environment, etc.) based on that data.

FIG. 4 is a flowchart showing an overview of steps for analyzingwellsite activity, according to the present disclosure. At start 400,one or more oil rig sites (such as the embodiments shown in FIGS. 1-2)may be carrying out various operations in the oilfield exploration andproduction process. For example, a wellsite may be engaged in drillingoperations, production operations, and/or logging/measurementoperations.

At step 410, data concerning wellsite operations may be recorded andaggregated together from various sources. Data may be generated andcaptured in real-time; for example, the Wellsite Information TransferSpecification (WITS) is a petroleum-industry standard for recording andtransmitting wellsite data such as, for example, hookload, drill torque,and weight-on-bit. Data may also include non-realtime-data, such asafter-the-fact work history reports. All data, regardless of source, maybe collected, aggregated, and stored in information handling systemsknown to those of skill in the art, such as the information handlingsystem embodiment of FIG. 3.

At step 420, errors or inconsistencies in the data collected in step 410may be corrected. Corrections may be made, for example, by manualadministrative intervention or by automated processes such asalgorithmic analysis of data consistency, cross-checking against otherdata sources, naive Bayes classification, or other data miningtechniques known to those of skill in the art. Corrections may alsoinclude standardization of data, such as standardizing data units andformat.

At step 430, the data collected in step 410 and corrected in step 420may be time aligned so that data from different sources may becorrelated together by time. In certain embodiments, time aligning thedata may include adjusting manually-logged timestamps for events in workhistory reports based on real-time data. For example, automaticallycollected real-time data concerning bit depth may be used to adjust awork-report event timestamp for picking up the bottom-hole assembly.Time alignment decisions may be made using, for example, process mapflow charts, probabilistic analysis, and optional manual administrativeintervention. In this way, data from various sources, including manuallyrecorded data, may be time aligned with the real-time data and anaccurate reconstruction of work history may be generated.

At step 440, output files may be produced. The output files mayoptionally reflect the raw collected data gathered in step 410, the dataafter the corrections of step 420, or the data after the time-alignmentprocess of step 430.

At step 450, one or more quality reports may be produced. The qualityreports may be based on an analysis of the output files generated instep 440. Quality reports may be used to flag results of pass/failcriteria, such as frequent motor stalls, excess weight-on-bit, etc.Additionally or alternatively, quality reports may be used forcomparative metrics, such as wellsite efficiency. Comparisons may bemade based on, for example, the equipment used, wellsitecharacteristics, operational decisions, and/or personnel.

At step 460, the data and reports collected and generated in step 410,420, 430, 440, and 450 may be shared. The data may be, for example,normalized for integration into a broader internal database.Additionally or alternatively, it may be sold externally to thirdparties.

At end 470, the steps shown in FIG. 4 may optionally be repeated tocollect, correct, analyze, and share additional data.

Each of the overview steps of FIG. 4 are discussed in further detailwith respect to FIGS. 5-17 below. Moreover, although the steps of FIG. 4are shown as discrete steps in a linear order, it may be understood inlight of the present disclosure that the steps may overlap or beperformed in a different order than the one shown. For example, outputfiles of corrected data (step 440) may be produced before the data istime aligned (step 430). Similarly, quality reports (step 450) may begenerated during data collection (step 410).

Aggregating and Pre-Processing Data

FIG. 5 is a flowchart showing one embodiment of steps for aggregatingand pre-processing data, according to the present disclosure. At start500, one or more wellsites may be carrying out various operations asdiscussed above with respect to step 400 of FIG. 4. During wellsiteoperations, data and measurements may automatically be generated inreal-time by various sources known to those of skill in the art, such aswellsite sensors, MWD/LWD tools, and software application logs.

At step 505, wellsite data may be automatically collected. An embodimentfor automatically collecting data is shown in FIG. 6 and discussedbelow.

At step 510, the collected data may be standardized to account fordifferences in formatting, units, naming conventions, etc. An embodimentfor standardizing the collected data is shown in FIG. 7 and discussedbelow.

At step 515, errors in the collected data may be identified andcorrected. An embodiment for identifying and correcting errors is shownin FIG. 8 and discussed below.

At step 520, all issues concerning the raw data collected in step 505,and the alterations made in step 515 to correct those issues, may bedocumented in a receipt file. An embodiment for documenting data issuesand alterations is shown in FIG. 9 and discussed below.

At step 525, a draft quality-assurance/quality-control (“QA/QC”) coverreport may be prepared based on the corrected data and a template thatspecifies the format of the report. Based on the template instructions,the report prepared in step 525 may contain some or substantially all ofthe corrected data from step 515 and the receipt file from step 520, forexample on a timestamp-by-timestamp basis. The template may also specifypass-fail or other evaluation criteria to apply to the data. Theevaluation may identify wellsite operation failures (or, in someembodiments, identify “not clearly pass” scenarios), such as frequentmotor stalls, excessive sliding time, inadequate cement measurements,etc. An embodiment for preparing reports is shown in FIG. 10 anddiscussed below.

At step 530, the draft QA/QC report produced in step 525 may be reviewedto determine whether it conforms to the expected template and reflectsan accurate comparison of the data against the evaluation criteria. Ifthe report contains an incorrect data presentation or an unexpectedevaluation result, for example because of an incorrect unit conversionin step 510 or a faulty error-correction decision in step 515, theadministrator may intervene to manually resolve any identified issues insteps 510, 515, or 520. Thereafter, steps 510 through 525 may be resumedto generate a draft QA/QC report and verify correct data presentationand evaluation.

At step 535, the draft QA/QC report produced in step 525 may be reviewedto determine whether the template used to produce the report may need tobe revised. For example, the review of step 535 may determine that adifferent data presentation is desired or that the pass-fail criteria isgenerating unhelpful or misleading wellsite operation evaluations. Ifnecessary the evaluation criteria may be revised to produce moreaccurate results, or the template may be revised to select differentevaluation criteria or data presentation. Thus, in step 530, the QA/QCreport may be reviewed to verify that data is being correctly processedand/or evaluated according to the selected template and evaluationcriteria; in step 535, the QA/QC report may be reviewed to verify theaccuracy and utility of the selected template and evaluation criteria.

At step 540, any template that was used for generating the QA/QC coverreport may be documented in a database. In this way, a record of thetemplates used to prepare the report may be preserved. Due to theverification in steps 530 and 535, it may be desirable to reuse the sametemplates for future QA/QC reports from the same data source orwellsite. Additionally or alternatively, documenting the templates mayfacilitate revision of all QA/QC cover reports produced using a specifictemplate in case of later revisions to that template.

At step 545, a final QA/QC cover report may be obtained based onsuccessful completion of the steps 525, 530, and 535 for producing andevaluating draft QA/QC cover reports, as described above.

At step 550, supplementary reports may be generated based on issuesflagged during the production of final QA/QC cover report in step 545.For example, a record of reoccurring issues may be generated to identifyand diagnose persistent wellsite operation errors. In situations whereinformation may be needed to allocate fault for a wellsite operationerror, supplementary reports may provide forensic reconstruction ofwellsite operations and measurements that may be used for root causeanalysis. For example, a supplementary report may be generated that maycompare the real-time data available to the wellsite operator at thetime of a wellsite operation error and compare it to the data followingthe standardization and error-correction process of steps 510, 515, and520. In situations where the raw data differs from the corrected datadue to, for example, calibration errors, an erroneous wellsiteoperations decision may thereby be traced to a failure to follow correctcalibration procedures.

At step 555, the corrected data generated in step 515 may be examined toidentify plausible drilling codes on a timestamp-by-timestamp basis. Thedrilling codes may, for example, be the drilling codes promulgated bythe International Association of Drilling Contractors for identifyingdrilling operations such as drilling on bottom, slides, rotations, etc.Algorithmic analysis may be performed on the corrected data to identifyplausible codes that are consistent with, for example, corrected dataconcerning bit depth, rotary torque, etc.

At step 560, a naive Bayes classifier may be used to assignprobabilities to each of the plausible codes (identified in step 555).As may be appreciated by those of skill in the art in light of thepresent disclosure, the naive Bayes classifier may be trained usingsample data sets matching drilling codes to various data parameters.Based on that training data, the naive Bayes classifier of step 560 mayuse probabilistic analysis of the available corrected data to assignlikelihoods to each of the plausible drilling codes. In this way, themost likely of the plausible drilling codes may automatically beidentified for each timestamp. If the probabilities assigned using thenaïve Bayes classifier do not reveal a dominant conclusion, anadministrator may be prompted to manually analyze the situation and makea decision using the interface.

At step 565, an output file may be generated comprising the aggregatedand pre-processed data according to the present method. The output filemay be formatted to be compatible with various software packages, suchas Halliburton's MaxActivity™ or WELLPLAN™ or Schlumberger's Petrel®.

Thus, according to the embodiment of FIG. 5, at end step 570 wellsiteoperations data may have been automatically collected, corrected, outputto cover reports, evaluated against pass-fail or other criteria,assigned probabilistic drilling codes, and/or formatted for use in othersoftware applications.

FIG. 6 is a flowchart showing one embodiment of steps for automaticallycollecting wellsite data, according to the present disclosure. The stepsmay begin at step 600 with a request for automatic data collection.

At step 605, an automation execution queue may be activated. Theautomatic execution queue may comprise an execution script and/or othersoftware program code that may identify the sources and methods forcollection of wellsite data.

At step 610, an interface method specified by the automatic executionqueue may be chosen for collecting and storing of the wellsite data. Forexample, the automatic collection routines may interface via file-typesknown to those of skill in the art (such as XML, CSV, TXT, etc.). Inalternative example embodiments, the chosen interface may be directaccess to a known database format, such as Halliburton's INSITE®, or viaJava's Abstract Window Toolkit. If direct database access is granted(internal/external), interface software may be used to read and writefrom the database automatically.

At step 615, a data source specified by the automatic execution queuemay be chosen for data collection. The data source may include a varietyof different sources known to those of skill in the art. For example,one possible data source known to those of skill in the art isnetwork-distributed data in the Wellsite Information Transfer StandardMarkup Language (WITSML) format. Additionally or alternatively, datasources may include, for example, 3rd-party databases, a network sharefolder, public regulatory information (which may be posted to theinternet), or e-mail. In addition to specifying the data source, theautomatic execution queue may also specify the frequency of dataretrieval. Real-time WITSML data, for example, may be retrieved everyminute or second, while public regulatory data may be collected once aweek or month.

At step 620, the automatic execution process may check the adequacy ofthe data collection. If sufficient information has been collected—forexample, if enough data for the desired reporting period has beencollected—the automatic collection of data may end at step 625. Ifsufficient information has not been collected, the collection ofinformation may be resumed.

At step 630, the data source may be contacted directly if necessary orappropriate. For example, if sufficient information had not beencollected at step 620 because of a data reporting failure (e.g., awebsite failing to update with expected new data), an automated attemptmay be made to communicate with the data source to resolve the error(e.g., an automated reconnect attempt). In this way, temporary datasource errors may be resolved in an automated way, without requiringadministrator intervention.

At step 635, the automatic data collection administrator may optionallyintervene if necessary or appropriate, such as in situations where theautomated error-recovery in step 630 is unsuccessful. The administratormay take remedial actions such as, for example, modifying the automaticexecution queue to identify alternative data sources or to removerequests for collection from a non-responsive data source. Regardless ofwhether the administrator exercises the option to intervene for manualcontrol, the automatic collection of data may resume at step 605 with anew cycle of the automatic execution queue.

FIG. 7 is a flowchart showing one embodiment of steps for standardizingcollected wellsite data, according to the present disclosure. The stepsmay begin at step 700 with a request to standardize collected data. Atstep 700, one or more data standardization templates may be provided,for example as library files available to the program software codeexecuting the subroutine shown as FIG. 7. The data standardizationtemplates may specify the desired type and format of standardized dataand may vary based on data type or intended data use. For example, atemplate may be provided for drilling operations that specifies apresentation format for drilling operation data such as weight-on-bit,rotary torque, bit-depth, etc. Another template may be provided forcementing operations that may specify the presentation format for flowrates, fluid volume pumped, cement-bond-logging data, etc. In this way,templates may describe how various types of data from one or more datasources may be formatted and presented for consistent recording andevaluation.

At step 705, the source of the data is scanned and identified. In oneembodiment, this may be, for example, information provided by theautomatic execution queue program code in steps 605, 610, and 615discussed above.

At step 710, it may be determined whether an available datastandardization template is known to match the data source identified instep 705. If a template is identified that matches the data sourceidentified in step 705, then that template may be applied in step 750.If a template is not identified as matching the data source, anautomatic template scanning and selection procedure may be initiated.

At step 715, the data may be scanned to identify data labelinginformation, such as for example wellsite data mnemonics labels.Mnemonics labels are used by those of skill in the art to describe thecontent of wellsite data measurements. Each mnemonic label may be usedto identify the data channel, the property being measured, and/or theunit being used to measure that property. For example, the mnemoniclabel DSF_PICK_UP_WEIGHT may identify a data channel reporting the pickup weight measured with the drillstring off the hole bottom and movingup; an associated property may be the Pickup_Hook_Load, the driller'sestimate of the average pickup hook load (pickup weight) over thereporting period; and the unit quantity may be HighForce, a unitreflecting either one thousand kilograms of force or one thousand poundsof force depending on the format of the reporting data channel.

Although mnemonics have standardized usages and definitions known tothose of skill in the art, in practice the application of mnemonics todata logging may be inconsistent and subject to error, including failingto include a mnemonic label or error in usage of proper measurementunits. Accordingly, one advantage of the present invention, discussedbelow, is the automatic analysis and standardization of mnemonic usageand labeling.

At step 720, the data may be scanned to identify unit names. Forexample, in the case of the DSF_PICK_UP_WEIGHT mnemonic discussed above,the HighForce unit name may be scanned to identify whether the datachannel reported using one thousand kilograms of force or one thousandpounds of force. At step 725, the data may be analyzed to scan foroverall data channel statistics, which may be used in step 730 for datarecognition.

At step 730, data shape recognition may be performed based on theresults of the scan for mnemonics, unit names, and data channelstatistics. Data shape recognition may be performed using mathematicalset theory. In certain embodiments, for example, drilling data mayidentify “in-slips” data using raw hookload data after interpreting howhookload measurements changed throughout the process of drilling thewell. Once such data sets are dissected into drilling stands, thePick-Up, Slack-Off, and Rotating Off-Bottom data may be identified usinga process flow map and the raw data. Drilling practice procedures,specific to each rig/customer, may be verified and explain anomalies inthe data. Good and bad procedures may follow characteristic shapes andresponses that populate a reference library. Those shapes may becompared in isolation in an automatic loop or array search. Localstatistics may be useful in analyzing isolated sets; in the case offailure, historical offset time data may be substituted, cross-checked,and verified to be useful. In certain embodiments of shape recognition,regression and analysis may be performed. Based on queries from theadministrator when outliers arise, complete libraries may be expandedover time to include obscure possibilities. Furthermore, by automaticanalysis of the overall data, errors in mnemonic labeling (or theabsence of mnemonic labeling) may be identified and corrected accordingto the present disclosure. For example, if a mnemonic label is presentbut the units are not identified, contextual data and other datastatistics may be used to predict the correct unit label. Similarly, ifa mnemonic label is incorrect or missing, information about the sourceof the data, contextual information from other data, and statisticalanalysis of the data may be used to make a prediction regarding thecorrect mnemonic label. Mathematical relationships may exist betweenparameters until discrete events occur. For example, strokes, flow, andpressure may be related by meta data; until the meta data changes as adiscrete time event, the relationship may hold pending other influencesthat may also be automatically identifiable. As another example, acalibration may be performed while drilling a well that may explain, forexample, why the Pick-Up, Slack-Off, Rotating-Off Bottom, andWeight-on-Bit are trending incorrectly either before or after thecalibration. Data from a wellsite may be incorrect or indicative ofissues that lead to useful interpretation. For example, hole cleaningissues, changes in mud properties, excessive tortuosity, etc., may causecharacteristic data shapes to show apparent errors in slack-off weighttrends. In this way, the root cause of the response may be identifiedaccording to the embodiments of the present disclosure.

At step 735, a naive Bayes classifier may be used to select the mostlikely associated standardization template based off the data analysisconducted in steps 715, 720, 725, and 730. As discussed above withrespect to the use of a naive Bayes classifier to assign probabilitiesto drilling codes (step 560 of FIG. 5), the classifier may be trainedusing sample data and the judgment of the administrator so as tominimize the end-user interaction.

At step 740, the results of the naive Bayesian classifier may bereviewed to determine whether the input data from step 705 has beenaccurately processed such that a likely matching template has beenproperly selected. As with the similar step 530 concerning reviewingQA/QC cover reports, if the result at step 740 is a determination thatautomated data processing and evaluation (steps 715 through 735) hasgenerated an erroneous template identification, an administrator maymanually intervene to correct the error, and the process may be resumedat step 715.

At step 745, the selected template may be reviewed to determine whetherthe template or the method used to select it may be in need of revision.If a revision to the template is needed, the revision may be made andthe automatic process of analyzing data may be resumed at step 715. Ifthe template is determined to not need revision, the process may proceedto step 750 where the template may be applied. The executing softwarecode may note the selected template so that when data from the samesource is evaluated in future iterations of step 710, the selectedtemplate may automatically be matched, obviating the need to repeatsteps 715 through 745.

At step 750, the selected template may be used to mine data from thesources identified in step 705. The template may specify which data tocollect, how often to collect it, etc.

At step 755, the data may be uniformly time-stamped according to thepresentation format specified by the template. For example, the templatemay specify the granularity of time-stamps and the order or format ofdate and time presentations.

At step 760, the data may be aggregated into a standardized data arraysequence as specified by the template. The selected template may includeinstructions concerning the presentation of the data within the dataarray sequence, such as the format of the data, the order of the data,etc.

At step 765, the units of data may be standardized. For example, datacollected from different sources that use different units (such as onedata source that uses kilograms and another that uses pounds) may bestandardized. This may involve conversion routines to convert data thatwas collected in one unit to another unit specified by the template.

The data standardization process may complete at step 770. Thus,according to the embodiment of FIG. 7, data collected from various,heterogeneous sources at different times may be aggregated, organized,and standardized into homogenous data arrays according to prescribedtemplate specifications. The mapping of input data to templates may beautomated through data analysis procedures and assisted by naiveBayesian classification, including the automated correction of missing,inconsistent, or erroneous mnemonic labeling.

FIG. 8 is a flowchart showing one embodiment of steps for identifyingand correcting errors in wellsite data, according to the presentdisclosure. The steps may begin at step 800 with a request to correcterrors in data, for example data collected according to the embodimentof FIG. 6 and standardized according to the embodiment of FIG. 7. Atstep 800, one or more data error lists may be provided, for example aslibrary files available to the program software code executing thesubroutine shown as FIG. 8.

Those of skill in the art of well-drilling may commonly review wellsitedata to identify errors and/or intentional or unintentionalmisrepresentation of data and events. Errors may fall into one or moreof at least four categories, as described below.

Incomplete data: data channels where information was collected but isincomplete because of, for example, loss of data, intermittent datatransmission, or erroneous starting and stopping of data recordation.

Inaccurate data: data channels where information was collected butappears to be inaccurate due to poor calibration, sensor drift, etc.This may be determined by looking at an individual data channel. Forexample, during the course of a single drilling run, a person ofordinary skill in the art would understand that hook load should remainrelatively constant withstanding several possible influencing changescomprising mud weight changes, sudden changes in trajectory, etc. Thus,if a hook load data channel reports changing values during the course ofa drilling run, this may be determined to reflect inaccurate sensordrift or inaccurate metadata, such as block weight, pipe weight per foot(as a function of grade and deterioration). Inaccurate data may also beidentified in by cross-referencing other data. In certain embodiments,data collected from various different sources may reflect similarmeasurements conducted using different settings; for example, multipledata channels may each contain torque measurements. Inconsistenciesbetween the measurements may reflect, for example, poor calibrationsettings or improper “zeroing” (e.g., taring) of one of the channels.This may be a frequent occurrence in differential measurements such asDifferential Pressure and Weight-On-Bit. In certain embodiments, astandard procedure may be implemented for collecting those points andenforced throughout. Nonetheless, variations in drilling practices existand templates may be configured to accommodate those changes.

Illogical data: data channels where information was collected butappears to be illogical. This may include data channels that areunexpectedly static, for example a data channel that reports that thedrilling apparatus is “in slips,” while other data channels showdrilling operations commencing. This may be due to software errors, suchas software incorrectly flagging a drill as being “in slips” whenpressure is put on the drill pipe from the traveling assembly in orderto drill a negative-weight well. Other illogical results may include,for example, large, unexpected discontinuities in bit-depth andhole-depth measurements.

Missing data: data channels or metadata where information was notcollected. This may be caused, for example, by a broken sensor,incorrect wellsite setup, data loss due to downhole tool failure, orhuman failure to record the information correctly.

The data error lists provided at step 800 may algorithmically define anautomated process for performing a review to identify errors in one ormore of the categories listed above, or in other categories known tothose of skill in the art. For example, a data error list may specifydata channels that may be cross-referenced for consistency. The dataerror lists may also specify how the error should be flagged for logginginto a receipt file, as discussed below with respect to the embodimentof FIG. 9. In certain embodiments, the data error lists may also provideremedial instructions for addressing the errors, such as specifying howinconsistencies in cross-checks should be resolved. The remedialprocedures specified in the data error lists may optionally includeinstructions for modifying the data to correct errors. By providinginstructions for diagnosing and resolving errors, data error lists maybe similar to virus definition lists known to those of skill in the art.

At step 805, the standardized data may be analyzed to select relevantdata error lists that should be applied to that channel.

At step 810, the standardized data may be scanned and the relevant dataerror lists applied. Where issues are detected, the instructions in thedata error list may be followed, including correcting the error if thedata error list so instructs. Any alterations to the input data may belogged.

At step 815, it may be determined whether any identified data anomalieswere not addressed by the available data error lists. If any suchanomaly exists, administrator control may be exercised to supplement thedata error list and the process may be resumed starting at step 805.Additionally or alternatively, the administrator may manually correctthe data.

At step 820, the complete raw and modified data sets may be created. Inthis way, the original data may be preserved but corrected data may beavailable for future analysis.

At step 825, the list of channels that were corrected may be appended tothe data set. This may be used to identify, for example, reoccurringdata channel errors so that remedial action may be taken.

The error detection and correction process may complete at step 830.Thus, according to the embodiment of FIG. 8, each data channel may beindependently analyzed for errors according to a list of known issues.Data channels may also be analyzed and cross-referenced againstinformation from other channels (especially duplicate channels withdifferent “tare” or “zeroing” settings) to identify irregularities.Remedial actions, including correcting the data, may be takenautomatically or through administrator intervention. The raw andcorrected data may be recorded in order to maintain a complete forensicrecord of wellsite operations.

FIG. 9 is a flowchart showing one embodiment of steps for documentingdata issues and alterations in a receipt file, according to the presentdisclosure. The steps may begin at step 900 with a request to documentdata issues and alterations. At step 900, raw and corrected data setsmay be provided, optionally including an appended channel-correctionlist, for example as discussed in the embodiment of FIG. 8.

At step 905, every issue identified during data standardization andcorrection process may be logged to a receipt file. The logging may beperformed on a channel-by-channel and timestamp-by-timestamp basis. Theissues logged may include, for example, identification of correctedmnemonics or units in the standardization process, discussed above withrespect to FIG. 7. It may also include, as further examples,identification of data issues flagged or errors corrected during theerror identification process, discussed above with respect to FIG. 8.

At step 910, the receipt file may optionally be used in disputeresolution. For example, as discussed above with respect to step 550 ofFIG. 5, information showing data that was available at the time ofwellsite operations decisions may be compared to corrected data toperform a forensic analysis of wellsite operation errors for thepurposes of fault allocation. It may also be possible to play back datafor the purposes of training, reprimand, legal compliance, etc. Thereceipt file generation process may complete at step 915.

FIG. 10 is a flowchart showing one embodiment of steps for preparing aQA/QC cover report from wellsite data, according to the presentdisclosure. The steps may begin at step 1000 with a request to prepare aQA/QC cover report, for example based on data corrected in accordancewith the embodiment of FIG. 8 discussed above. At step 1000, a QA/QCcover report template may be provided. The template may compriseinstructions for the format and presentation of the report. For example,the template may specify key data to be emphasized in the report.

The template may also link to data evaluation criteria. Criteria mayinclude, for example, Kanban presentations, a visual process managementsystem known to those of skill in the art. The evaluation criteria mayalso include pass-fail criteria, such as for example verificationcriteria that determines whether expected data was properly collected(e.g., casing information data). Criteria may also include analyticalroutines to determine whether, for example, drilling occurred withindogleg limitations and/or whether drilling occurred outside of thepayzone.

At step 1005, the report may be created according to the templateinstructions, including the automated construction of visual Kanbanpresentations, color-coded analysis against the pass-fail criteria, andthe call-out of selected key data.

At step 1010, the need for supplementary reports may be flagged (orsupplementary reports may be created) based on the results of thepass-fail criteria evaluation. This may include supplementary reportsproviding more detailed data concerning a “fail” result on a pass-failcriteria, as well as supplementary reports directed toward “not clearlypass” results in order to anticipate potential future issues.

At step 1015, administrative control may be exercised if necessary, forexample because of an unexpected failure of a pass-fail criteriaevaluation. The administrator may take remedial actions to correct anyerror in generating the report, and then resume the process ofgenerating the report at step 1000. If no administrative intervention isnecessary, a report may be output and the process completed at step1020.

Macroscopic Report Interpretation

FIG. 11 is a flowchart showing one embodiment of steps for macroscopicreport interpretation, according to the present disclosure. Althoughdata automatically generated during wellsite operations may have beencollected in real-time by the procedures described above with respect toFIG. 5, additional wellsite data may be recorded in macroscopic wellsitereports that may not be provided in real-time. Such reports may includewellsite metadata describing operating parameters, such as for examplethe type of drillbit used, casing information relating to depths andsizes, bottom-hole assembly information, and other parameters known tothose of the art. The wellsite reports may also include data reflectingwellsite operations during flat time (i.e., time during which real-timesensors are not changing), such as for example while the rig is beingrepaired. The macroscopic reports may also include the wellsiteoperators' manual logs of operations.

The steps necessary for interpretation of a macroscopic report may varybased on the complexity and irregularity of the macroscopic report. Forexample, information may be automatically collected from relativelysimple macroscopic reports using standard optical character recognitiontechniques. Additionally or alternatively, digital format macroscopicreports may include metadata tags (e.g., XML tags) that facilitate theautomatic extraction of report content. In such reports, data may beextracted using methods known to those of skill in the art.

On the other hand, metadata reports may contain more complex datapresentations, lack metadata tags, and/or have irregular content andstructure that may be difficult to automatically parse. As discussed inmore detail below, the strategy of interpreting such reports may rely,for example, on analysis of report characteristics, such asidentification of pixel lines that outline tables in rectangular andnon-rectangular form. Similarly, a table in a report may comprise boxeswith identifiable shape, width, height, line-thickness, shading, andother characteristics, and tables of boxes may be aligned adjacentlywith similar positions. The juxtaposition of tables may be static evenif their page placement is dynamic depending on the amount of content inprevious tables. Thus, a given macroscopic report may have acharacteristic “fingerprint” that may aid in the identification andcross-reference of rules previously-used to gather information fromsimilar macroscopic reports. Information that does not match anyexisting “fingerprint” or have associated logic for automaticinterpretation may alert the administrator to provide one-time guidanceor, alternatively, new interpretation rules to apply in the future. Thedata typically contained in macroscopic wellsite reports may thereforebe automatically collected and interpreted according to the embodimentof FIG. 11.

At start 1100 one or more wellsites may have carried out variousoperations, as discussed above with respect to step 400 of FIG. 4 andstep 500 of FIG. 5, and data concerning those operations may have beenprovided in one or more macroscopic reports. If the report wasoriginally in a hardcopy format, at step 1100 it may have been scannedinto a computer-readable format, such as PDF or BMP. Additionally,templates for the automatic extraction of data from the macroscopicreports may be provided in a template database. As will be discussed ingreater detail with respect to FIGS. 12 and 13, macroscopic reports maynot necessarily be of a fixed format. They may instead vary, or“accordion,” in length depending on the amount and type of content. Atemplate may define procedures for automatically interpreting a report,such as optical character and table recognition, and provideinstructions for processing and extracting data from the report. Forexample, reports from a particular oilfield services company may have asubstantially similar format, and so a specific template for processingreports from that company may be developed.

At step 1105, a macroscopic report may be analyzed to determine whethera matching template exists. If a template is found, it may be used instep 1125. If a matching template is not found, the template mappingprocedure of step 1110 may be used.

At step 1110, an automatic template mapping procedure may be used tocreate a template for the report analyzed in step 1105. An embodimentfor template mapping is shown in FIG. 12 and discussed below.

At step 1115, administrative control may optionally be exercised if thetemplate mapping procedure is not successful. For example, theadministrator may manually intervene to correct errors produced by theautomatic mapping procedure. If administrator intervention is necessary,the mapping procedure may be resumed at step 1110.

At step 1120, the template that has been created by the automaticmapping process may be logged into a template database, such that thecorrect template will be found when the matching template analysis isperformed again at step 1105.

At step 1125, the template “accordion method” may be used to map themacroscopic report with the matched template. As discussed above, amacroscopic report may grow or shrink in size (“accordion”) because ofthe type and quantity of data contained in any given report. A templatemay be able to adjust dynamically in order to map all of the data in agiven report.

At step 1130, an evaluation is made as to whether the template used instep 1125 was a success. For example, a macroscopic report may add a newtype of data or change its presentation of an old type of data. Underthose circumstances, a previously matched template may need to beupdated in order to map the new data presentation. Thus, at step 1130,if a matched template is not able to map the macroscopic report, theprocess may proceed to step 1110 to revise the template mapping. If thetemplate “accordion method” is successful, however, the process mayproceed to step 1135 to extract data from the macroscopic report.

At step 1135, information may be extracted from the macroscopic report.An embodiment for information extraction is shown in FIG. 13 anddiscussed below.

At step 1140, administrative control may optionally be exercised if thedata extraction procedure is not successful. For example, theadministrator may manually intervene to correct errors produced by theextraction procedure. If administrator intervention is necessary, themapping procedure may be resumed at step 1140.

At step 1145, the extracted data may be reviewed to assign plausibledrilling or other codes on a timestamp-by-timestamp basis. As discussedabove with respect to step 555, algorithmic analysis may be performed onthe macroscopic report data to identify plausible drilling codes thatare consistent with the collected data. Then, as discussed above withrespect to step 560, a naive Bayes classifier may be used to assignprobabilities to each of the plausible codes such that the most likelyof the plausible drilling codes may automatically be identified for eachtimestamp.

At step 1150, the information extracted from the macroscopic reports, aswell as the drilling code probabilities prepared in step 1145, may beexported, for example into an SQL database.

Thus, according to the embodiment of FIG. 11, at end step 1155 wellsiteoperations data may have been automatically extracted from macroscopicreports, assigned probabilistic drilling codes, and/or organized into astandardized output format for use in other software applications.

FIG. 12 is a flowchart showing one embodiment of steps for templatemapping a macroscopic report, according to the present disclosure.Although the flowchart of FIG. 12 is discussed below with respect to asingle macroscopic report, multiple similar macroscopic reports may beprocessed. In certain embodiments, before using the embodiment of FIG.11 in an operational environment, the embodiment of FIG. 12 may beapplied to numerous sample reports in order to ensure that robusttemplates are developed for mapping similar reports in the future.

The steps may begin at step 1200 with a request to template map a report(or, as discussed above, multiple reports). The steps 1205 through 1235described below may be performed, for example, by image processingtechniques known to those of skill in the art, such as optical characterrecognition and pixel analysis. Where available, the described steps mayalso take advantage of electronic metadata included with the report.

At step 1205, the report may be analyzed to detect dividers, such aspage breaks, horizontal lines, vertical lines, and tables.

At step 1210, the report may be analyzed to identify languagecharacteristics, such as font sets used, whether information ispresented top-down and left-to-right, etc.

At step 1215, a filter grid may be used to identify each of the varioussections of the reports based on the dividers identified in step 1205and the language characteristics determined in step 1210. For example,it may be determined that one section of the report may comprise aspecific header followed by a series of tables.

At step 1220, a subprocess comprising, for example, substeps 1221through 1233 may be repeated for each of the sections identified in step1215. Each iteration of the subprocess of step 1220 may generate amapping table describing the presentation of information for a section.For example, the mapping table may specify the organization, format, anddata content of the section. The mapping table may be used in step 1115and 1135 discussed above to map the same section in another macroscopicreport and extract information. The subprocess of step 1220 may berepeated for each of the sections identified in step 1215 so that amapping table of features may be created for each of the report sectionsidentified in step 1215.

At substep 1221, the section selected in step 1220 may be analyzed todetermine whether it has a fixed or variable page number. For example, areport summary section identifying the report date and wellsite locationmay always appear on the first page of the report. By comparison, areport section summarizing the results of a particular drilling run mayappear on different pages in different reports.

At substep 1222, report headers and footers are analyzed, for example todetermine whether they repeat. If headers and footers (for example apage number) are repeated on multiple pages of a report, they may beexcluded from the evaluation of any particular section.

At substep 1223, logos may be detected and their location recorded. Thismay be useful, for example, if information about the source of thereport may be extracted from the logo. On the other hand, if the logodoes not contain useful information, recording its position and locationmay allow it to ignored when mapping the report.

At substep 1224, date and location fields may be identified such that ifthe section presents information specific to a certain date or location,that information is mapped for extraction. Similarly, at substep 1225,wellsite information fields may be identified and mapped for extraction.In this way, the remaining data in the section may later be related tothe specific date, location, and wellsite information mapped in sections1224 and 1225.

At substeps 1226-1233, the remaining data in the section may be mapped.For example, by identifying secondary and tertiary subheadings, unitsubheadings, and table specifications (such as vertical and horizontaldividers, row and column numbers, cell color-coding, and line-widthchanges). In certain embodiments, a specific template may storeinformation regarding a minimum, maximum, and typical number of pages aswell as characteristic paper size and orientation.

Additional substeps may be added to step 1220 according to the needs andformat of particular macroscopic reports. In this way, a mapping tablemay be created for each possible section that specifies the anticipatedorganization of data for retrieval in the extraction process of step1135. The mapping table may be dynamic so that even if a section in aspecific macroscopic report being mapped “accordions” from the reportused to build the mapping table—such as by containing more or less datain the section, containing more than one of the same section (forexample, repeating the same section for different dates or differentwellsites), or omitting the section—all data present may be mapped forextraction.

At end step 1235, the collection of mapping tables generated bysuccessive iterations of step 1220 may be aggregated to create a mappingtemplate.

FIG. 13 is a flowchart showing one embodiment of steps for templatemapping a macroscopic report, according to the present disclosure.Although the flowchart of FIG. 12 is discussed below with respect to asingle macroscopic report, multiple similar macroscopic reports may beprocessed. For example, before using the embodiment of FIG. 11 in anoperational environment, the embodiment of FIG. 12 may be applied tonumerous sample reports in order to ensure that robust templates aredeveloped for mapping similar reports in the future.

At step 1300, a macroscopic report has been matched to a template, asdiscussed with respect to step 1105, and the template has beensuccessfully used to map the report, as discussed with respect to step1130.

At step 1305, the mapping template may be used to mine information fromthe macroscopic report and store it in a standardized output format,such as an SQL database. The mining process may comprise, for example,using information stored in the mapping template to identify thelocation and format of relevant data in the report. In certainembodiments, the mapping template may be used to identify a table in thereport, and the boundaries between rows and columns may automatically beidentified by, for example, the detection of horizontal and verticallines. The mapping template may specify the significance of the data ineach cell of the table; for example, a template may specify that eachrow of a table contains casing parameters and that the first columnspecifies casing depth and the second casing size.

At step 1310, administrative control may optionally be exercised ifnecessary. For example, if the automatic detection process encountersunexpected data, the administrator may manually intervene to determinehow to extract the data, for example by adding additional SQLcontainers. The information mining may then resume at step 1305.

At step 1315, positional relationships between data may also be noted,for example by adding information to the SQL database. This may providecontext that may be useful for later evaluation of the data, for exampleduring the naive Bayesian classification at step 1145.

At step 1320, if the information mined at step 1305 is not already in areadable format, optical-character recognition processes known to thoseof skill in the art may be applied. This step may be aided byinformation contained in the template; for example, the template mayspecify language or font information that may be useful for accurateoptical-character recognition.

At step 1325, administrative control may optionally be exercised ifnecessary. For example, if a specific character cannot be automaticallyrecognized by optical-character recognition procedures, theadministrator may manually identify the character. If more significantissues with the data mining are identified, for example if the templatemapping fails to properly identify data content based on context, thenthe administrator may optionally revise the parameters or templates usedfor the mapping and extraction procedure, and it may resume at step1300.

At end step 1330, information extracted from the macroscopic report, aswell as relevant context information, may be stored in an SQL database.

Time-Aligning Data and Verifying Interpretation

FIG. 14 is a flowchart showing one embodiment of steps for time-aligningdata and verifying interpretation, according to the present disclosure.At step 1400, real-time data may have been collected and pre-processed,for example according to the embodiment of FIG. 5 discussed above, andmacroscopic report data may have been extracted and interpreted, forexample according to the embodiment of FIG. 11 discussed above.

At step 1405, the data from all available sources may be aggregated andorganized into a common database. The data may be arranged into a commontime series based on the available time-stamps so that information fromreal-time data sources is aligned with data from the same time-stamps inthe macroscopic reports. For example, a macroscopic report indicating adrilling operation was conducted during a certain time may be alignedwith all real-time data collected during that time relating to, e.g.,weight-on-bit, torque, etc.

At step 1410, non-linear process map models may be used to correct andadjust incongruous time series data or identify operationalirregularities. For example, although the real-time and macroscopic datamay be collected and time-stamped, the information may not beconsistent. The data in macroscopic reports may be described only ingranularity of 15- or 30-minute intervals (compared to minute or secondgranularity for real-time data), may be manually logged, and/or may notbe logged until several hours after relevant events, leading topotential ambiguities and inaccuracies. Non-linear process map modelsmay be used to resolve those apparent ambiguities or inaccuracies. Acommercially available software package that may be used to implementnon-linear process map models is Stateflow® by MathWorks. An embodimentfor using non-linear process map models is shown in FIG. 15 anddiscussed below.

At step 1415, administrative control may optionally be exercised ifnecessary. For example, if ambiguities in the time-aligned data are notautomatically resolved by following the non-linear process map, anadministrator may intervene to manually correct the information.Following any necessary administrative intervention, the time-alignmentprocedure may resume at step 1405.

At step 1420, the time-aligned data may be used to create an operationsreport. The operations report may provide a comprehensive summary of allknown wellsite information based on the time-aligned real-time andmacroscopic report data. For example, the report may present a summaryof all wellsite operator activities as well as associated, relevantreal-time data measured during those activities.

At step 1425, any issues identified during the process of time aligningthe data (steps 1405 through 1415) and/or preparing the operationsreport (step 1420) may be documented in a receipt file. The preparationof a receipt file may be similar to the steps taken for preparing areceipt file after correcting the real-time data, as discussed abovewith respect to FIG. 9.

At step 1430, the aggregated, time-aligned data may be analyzed using anaive Bayesian classifier to assign probabilities to plausible drillingor other codes on a timestamp-by-timestamp basis. This process may besimilar to the steps taking for performing similar Bayesian analysis ofthe real-time data (steps 555 and 560) and macroscopic report data(1145), discussed above.

At step 1435, administrative control may optionally be exercised ifnecessary to manually override the results of the naive Bayesianclassifier.

Thus, according to the embodiment of FIG. 14, at end step 1440 acomplete record of wellsite operations data may have been aggregatedfrom real-time and macroscopic reports, time-aligned and corrected, andassigned probabilistic drilling codes.

FIG. 15 is a flowchart showing one embodiment of steps for following anon-linear process map, according to the present disclosure. At step1500, real-time and macroscopic data has been aggregated into apreliminary time-alignment based on the available time stampinformation. Additionally, non-linear process maps may have beencreated, for example using Stateflow®, that correspond to wellsiteoperation procedures. In this way, the time-aligned well-site data maybe imported into Stateflow® so that a user may “watch” a visualrecreation of the wellsite operation procedures reflected by thetime-aligned data. For example, the time-aligned data for a standarddrilling operation may include hundreds or thousands of datapoints. Butby importing that data into an appropriately designed non-linear processmap, a high-level animation may be created that shows the drill operatorperforming routine steps such as making a connection while the drillpipe is in slips, picking it up out of the slips, beginning rotation andlowering to bottom, and starting drilling once at bottom.

At step 1505, corrections and adjustments may be made to thetime-aligned data. Although various adjustments may be made, inparticular, adjustments may be made to correct ambiguous or potentiallyinaccurate data from macroscopic reports based on clarifying real-timedata. For example, a wellsite operator may have misrecorded the timethat a drilling operation began, but that error may be corrected bycomparing to the real-time data showing precise timestamps when, e.g.,drill rotation was observed.

At step 1510, the drilling code probabilities previously assigned by thenaive Bayesian classifier (for example in steps 560 for the real-timedata and 1145 for the macroscopic report data) may be recalled for thetime-aligned data.

At step 1515, the drilling code probabilities for each data set may beanalyzed and correlated into changes in state on the non-linear processmap. In this way, a state flow timing and sequence record may beconstructed for each respective data set. Thresholds may be set so thatstate changes occur only after clear confirmation from the probabilisticdrilling code information. Thus, temporary incorrect drilling codepredictions (which may be caused by inaccurate information) may beignored in the state flow timing and sequence record.

At step 1520, the state machine timing and sequence record may be cycledthrough in the non-linear process map. Records may be created of anydisagreement between the state machine generated from the real-time dataas compared to the state flow generated from the macroscopic reportingdata. In certain embodiments, algorithmic routines may be provided toautomatically adjust time alignments in the event of any disagreement.

At step 1525, administrative control may optionally be exercised ifnecessary where records of disagreement between the two datasets areobserved in step 1520. A disagreement may be caused by improper timealignment or incorrect assignment of probabilities to drilling codes bythe naive Bayesian classifier. The administrator may intervene tofurther correct the time alignment or may manually override theautomatically-predicted drilling codes. Review may resume at step 1510.

At step 1530, any remaining irregularities in time-alignment between thereal-time and macroscopic datasets may be the result of errors in thenon-linear process map. A list of timestamps where irregularities remainmay be created.

At step 1535, the non-linear process map may be reviewed to determinewhether the irregularities noted in step 1530 are caused by errors inthe non-linear process map. If so, the non-linear process map may beamended to correct the error, and the time-alignment process may beresumed at step 1505.

At end step 1540, a complete record of wellsite operations data, fromreal-time and macroscopic reports, may have been aggregated andtime-aligned.

Data Analysis and Reporting

FIG. 16 is a flowchart showing one embodiment of steps for data analysisand reporting, according to the present disclosure. At step 1600,real-time and macroscopic report data may have been collected,corrected, and time-aligned, for example according to the embodiment ofFIG. 14 discussed above; a QA/QC cover report may have been prepared,for example according to the embodiment of FIG. 5 at steps 525 through545; and an operations report may have been prepared, for exampleaccording to the embodiment of FIG. 14 at step 1420.

The data analysis and reporting steps described below may include dataanalysis and reporting processes known to those of skill in the art.However, such data analysis and reporting may be difficult and timeconsuming when data may be fragmented across different data sources,unstandardized, and/or contain errors. Thus, data analysis and reportingmay be improved due to the availability of collected, corrected, andtime-aligned data as discussed above. Moreover, because of theavailability of homogenized datasets, manual analysis may be replaced byautomated algorithmic procedures.

At step 1605, well log reports may be automatically produced using thetime-aligned data, or pre-existing reports (for example, the drillerdiary of torque and drag measurements) may be supplemented.Additionally, the interpretation of the well log reports may beautomated by algorithmic data analysis procedures known to those ofskill in the art.

At step 1610, invisible lost time (“ILT”) calculations may be performed.ILT calculations may identify relatively small drilling operationinefficiencies that may aggregate to significant lost time. For example,an ILT analysis may identify that during a particular type of drillingoperation, one oil rig regularly spends three minutes in slips, whileother oil rigs in the same area spend an average of only forty-fiveseconds in slips.

Although ILT analysis is known to those of skill in the art, currentapproaches may use relatively static analysis for ILT calculations. Forexample, current ILT analysis may consider only rigid, single-dimensionmeasurements such as evaluating time drilling on bottom separately fromtime drilling circulating. The availability of time-aligned andhomogenized data, according to the present disclosure, may enable moredynamic calculations. For example, a hierarchical process map may becreated in process map software, such as Stateflow® discussed above withrespect to the embodiment of FIG. 15. The hierarchical process map maydefine both states and substates so that dynamic analysis may beperformed. For example, a top level timing event state may trigger fordrilling on bottom, then various substates may be individually activatedand timed for rotating, sliding, or circulating. In this way, withproperly defined process maps, ILT analysis may be performed so as todynamically cross-correlate various related data.

At step 1615, the statistical significance of ILT claims may beevaluated. For example, ILT calculations from a small sample ofwellsites (such as calculations made in step 1610) may be used to makebroader claims regarding wellsite performance. The statisticalsignificance of those claims may be evaluated, for example usingalgorithmic statistical evaluations known to those of skill in the art,in order to determine whether extrapolations from a smaller sample aresupported.

At step 1620, the statistical significance of financial ILT claims maybe evaluated. ILT calculations, for example from steps 1610 or 1615, maybe used to predict financial impacts. In this way, the cost resultingfrom operational inefficiencies measured by ILT may be estimated, andcosts may be projected, for example on a yearly basis or for multiplewellsites. This data may be used to quantify the costs fromunderperforming wellsites. The statistical significance of those costestimates may be evaluated, for example using algorithmic statisticalevaluations known to those of skill in the art. The cost informationgenerated by this ILT analysis may be aggregated across multiplewellsites and indexed based on, for example, drilling codes. In thisway, it may be possible to estimate costs for individual drilling codesby looking at historical ILT calculations associated with that code.

At step 1625, cost data may be used for evaluating applications forexpenditure. For example, wellsite operators may be asked to estimatethe value associated with potential wellsite projects; this may beuseful in making a competitive bid to provide services. Persons of skillin the art may be able to evaluate a potential wellsite project anddetermine the steps that may need to be taken during the project.Aggregated historical cost data, for example the cost data generated instep 1620, may be used to estimate the cost for each step that may beanticipated for the potential project. The anticipated costs may be usedas a penalty function in assessing the value of the project and may, forexample, affect a decision about the amount of a competitive bid.

At step 1630, quality reports may be generated and optionally storedinto a global performance database. The quality reports may summarizethe operational decisions made during the inquiry period and how theyimpacted wellsite efficiency. The quality reports may alsocross-reference other data, such as comparing actual measuredoperational decisions with the drilling decisions that would have beenanticipated based on available information. The data may be mined andpresented from a variety of different perspectives. In certainembodiments, a quality report may focus on the operations of a specificwellsite to summarize information concerning the quality of wellsiteoperations. In other embodiments, a quality report may focus on aspecific drill operator and report on efficiency metrics across thevarious wells on which that drill operator worked. In other embodiments,a quality report may focus on a particular component, such as a specificbit design, and present information on efficacy in various wellsiteconfigurations and use cases. The quality reports may be used to improveoperational efficiency, for example by diagnosing recurring operationalinefficiencies that may be ameliorated through targeted training.Similarly, the reports may be used to identify team members orcomponents with specific advantages—for example a drilling operator ordrill bit particularly effective in a certain type of wellsiteenvironment—that may be desirable for the particular needs of a project.

At step 1635, administrative control may optionally be exercised ifnecessary, for example because of errors in generating reports based onthe input data. The administrator may intervene to take remedial action,such as by revising the routine that generated the error or manuallyoverriding erroneous results. The reporting process may resume at startstep 1600.

At step 1640, the reports generated in steps 1605 through 1635 may beoutput into useful formats. This may include an output file formatted tobe compatible with various software packages, such as Halliburton'sMaxActivity™ or WELLPLAN™ or Schlumberger's Petrel®, as discussed abovewith respect to step 565. Moreover, the data included in the output mayvary based on the intended audience. For example, an executive may wishto receive a higher-level summary of all operations; a wellsite operatormay wish to receive granular data about a specific well; a drill bitsalesman may wish to receive performance information only for specificdrill bits. The output may also be contextual. For example, a wellsitereport may include generally summary data, but any operational failuremay be flagged and a supplemental report provided with specific detailsassociated with that failure.

At step 1645, the output data of step 1640 may automatically beassimilated into native software. Different departments or organizationsmay use different software packages for managing data. The output dataof step 1640 that is compatible with a particular software package mayautomatically be assimilated into that package.

At step 1650, wellsite data may be used to evaluate experimentaltechniques. For example, experimental autodriller software algorithmsmay be tested against measured data to determine how the autodrillerwould have responded in real-time. The simulated autodriller operationaldecisions may be compared to the actual operational decisions made atthe wellsite. If the autodriller is observed to take suboptimal coursesof action in response to the observed data, the experimental algorithmsmay be adjusted. Alternatively, the autodriller decisions may beobserved to be equivalent or superior to the decisions made by the drilloperator.

Thus, according to the embodiment of FIG. 16, at end step 1655time-aligned wellsite operations data may have been used to create andinterpret well logs; perform ILT calculations and test the statisticalsignificance of claims generated from those calculations; create ILTcost information for each drilling code, which may be used assess valuein applications for expenditure; and produce various quality reports.The aggregated data may be exported into native and third-party usableformats, automatically assimilated into various tools, and may also beused to validate experimental auto-driller software.

Although the steps of FIGS. 5-16 are shown as discrete steps in a linearorder, it may be understood in light of the present disclosure that thesteps may overlap or be performed in a different order than the oneshown.

An embodiment is a comprising collecting wellsite data from a pluralityof sources, standardizing the wellsite data, correcting the wellsitedata, time aligning the wellsite data, and producing a report based onthe wellsite data.

The plurality of sources may optionally comprise a real-time data sourceand a macroscopic reports. Collecting wellsite data from the macroscopicreport may comprise selecting a template associated with the macroscopicreport and using the template to extract the data from the macroscopicreport. Time aligning the wellsite data may comprise changing a firsttimestamp associated with a first wellsite measurement value taken fromthe macroscopic report based on a second timestamp associated with asecond wellsite measurement value taken from the real-time data source.

Standardizing the wellsite data may comprise identifying at least onesource of the wellsite data; selecting a template associated with the atleast one source, wherein the template comprises instructions forprocessing wellsite data from that source; and applying the template tothe wellsite data. The instructions for processing wellsite data mayoptionally comprise instructions for creating a data array sequenceusing the wellsite data.

Correcting the wellsite data may comprise selecting one or more dataerror lists associated with the wellsite data, wherein the data errorlists comprise one or more instructions for identifying and correctingan error in the wellsite data; and applying the data error lists to thewellsite data to identify and correct one or more errors in the wellsitedata. The instructions may optionally comprise at least one instructionfor identifying and correcting an error in the wellsite data bycomparing a first wellsite measurement value to a second wellsitemeasurement value. The first wellsite measurement value may optionallybe obtained from a difference source than the second wellsitemeasurement value.

Producing the report may comprise selecting a template, wherein thetemplate specifies one or more pass-fail criteria; determining a resultby evaluating the wellsite data using the pass-fail criteria; andrecording the test result in the report.

An embodiment is an information handling system comprising a memorydevice communicably coupled to a processor, the memory device containinga set of instruction that, when executed by the processor, cause theprocessor to collect wellsite data from a plurality of sources,standardize the wellsite data, correct the wellsite data, time align thewellsite data, and produce a report based on the wellsite data.

The plurality of sources may comprise a real-time data source and amacroscopic report. The set of instructions that cause the processor tocollect wellsite data from at least one macroscopic report mayoptionally further cause the processor to select a template associatedwith the macroscopic report and use the template to extract the wellsitedata from the macroscopic report. Additionally or alternatively, the setof instructions that cause the processor to time align wellsite data mayoptionally further cause the processor to change a first timestampassociated with a first wellsite measurement value taken from themacroscopic report based on a second timestamp associated with a secondwellsite measurement value taken from the real-time data source.

The set of instructions that cause the processor to standardize thewellsite data may optionally further cause the processor to: identify atleast one source of the wellsite data; select a template associated withthe at least one source, wherein the template comprises templateinstructions for processing wellsite data from the source; and apply thetemplate to the wellsite data. The template instructions may compriseinstructions for creating a data array sequence using the wellsite data.

The set of instructions that cause the processor to correct the wellsitedata may optionally further cause the processor to: select one or moredata error lists associated with the wellsite data, wherein the dataerror lists comprise one or more error-list instructions for identifyingand correcting an error in the wellsite data; and apply the data errorlists to the wellsite data to identify and correct one or more errors inthe wellsite data. The error-list instructions may comprise at least oneerror-list instruction for identifying and correcting an error in thewellsite data by comparing a first wellsite measurement value to asecond wellsite measurement value. The first wellsite measurement valueoptionally may have been obtained from a different source than thesecond wellsite measurement value.

The set of instructions that cause the processor to produce the reportmay optionally further cause the processor to select a template, whereinthe template specifies one or more pass-fail criteria; determine aresult by evaluating the wellsite data using the pass-fail criteria; andrecord the result in the report.

Therefore, the present disclosure is well adapted to attain the ends andadvantages mentioned as well as those that are inherent therein. Theparticular embodiments disclosed above are illustrative only, as thepresent disclosure may be modified and practiced in different butequivalent manners apparent to those skilled in the art having thebenefit of the teachings herein. Furthermore, no limitations areintended to the details of construction or design herein shown, otherthan as described in the claims below. It is therefore evident that theparticular illustrative embodiments disclosed above may be altered ormodified and all such variations are considered within the scope andspirit of the present disclosure. Also, the terms in the claims havetheir plain, ordinary meaning unless otherwise explicitly and clearlydefined by the patentee. The indefinite articles “a” or “an,” as used inthe claims, are defined herein to mean one or more than one of theelement that it introduces. Additionally, the terms “couple”, “coupled”,or “coupling” include direct or indirect coupling through intermediarystructures or devices.

What is claimed is:
 1. A method comprising: collecting wellsite datafrom a plurality of sources; standardizing said wellsite data;correcting said wellsite data; time aligning said wellsite data; andproducing a report based on said wellsite data.
 2. The method of claim1, wherein said plurality of sources comprises a real-time data sourceand a macroscopic report.
 3. The method of claim 2, wherein collectingwellsite data from said macroscopic report comprises: selecting atemplate associated with said macroscopic report; and using saidtemplate to extract said wellsite data from said macroscopic report. 4.The method of claim 2, wherein time aligning wellsite data comprises:changing a first timestamp associated with a first wellsite measurementvalue taken from said macroscopic report based on a second timestampassociated with a second wellsite measurement value taken from saidreal-time data source.
 5. The method of claim 1, wherein standardizingsaid wellsite data comprises: identifying at least one source of saidwellsite data; selecting a template associated with said at least onesource, wherein said template comprises instructions for processingwellsite data from said source; and applying said template to saidwellsite data.
 6. The method of claim 1, wherein correcting saidwellsite data comprises: selecting one or more data error listsassociated with said wellsite data, wherein said data error listscomprise one or more instructions for identifying and correcting anerror in said wellsite data; and applying said data error lists to saidwellsite data to identify and correct one or more errors in saidwellsite data.
 7. The method of claim 6, wherein said instructionscomprise: at least one instruction for identifying and correcting anerror in said wellsite data by comparing a first wellsite measurementvalue to a second wellsite measurement value.
 8. The method of claim 7,wherein said first wellsite measurement value was obtained from adifferent source than said second wellsite measurement value.
 9. Themethod of claim 1, wherein producing said report comprises: selecting atemplate, wherein said template specifies one or more pass-failcriteria; determining a result by evaluating said wellsite data usingsaid pass-fail criteria; and recording said result in said report. 10.The method of claim 1, wherein said report is based on wellsite datafrom a plurality of wellsites.
 11. An information handling systemcomprising: a memory device communicably coupled to a processor, thememory device containing a set of instruction that, when executed bysaid processor, cause said processor to: collect wellsite data from aplurality of sources; standardize said wellsite data; correct saidwellsite data; time align said wellsite data; and produce a report basedon said wellsite data.
 12. The system of claim 11, wherein saidplurality of sources comprises a real-time data source and a macroscopicreport.
 13. The system of claim 12, wherein said set of instructionsthat cause said processor to collect wellsite data from at least onemacroscopic report further cause said processor to: select a templateassociated with said macroscopic report; and use said template toextract said wellsite data from said macroscopic report.
 14. The systemof claim 12, wherein said set of instructions that cause said processorto time align wellsite data further cause said processor to: change afirst timestamp associated with a first wellsite measurement value takenfrom said macroscopic report based on a second timestamp associated witha second wellsite measurement value taken from said real-time datasource.
 15. The system of claim 11, wherein said set of instructionsthat cause said processor to standardize said wellsite data furthercause said processor to: identify at least one source of said wellsitedata; select a template associated with said at least one source,wherein said template comprises template instructions for processingwellsite data from said source; and apply said template to said wellsitedata.
 16. The system of claim 11, wherein said set of instructions thatcause said processor to correct said wellsite data further cause saidprocessor to: select one or more data error lists associated with saidwellsite data, wherein said data error lists comprise one or moreerror-list instructions for identifying and correcting an error in saidwellsite data; and apply said data error lists to said wellsite data toidentify and correct one or more errors in said wellsite data.
 17. Thesystem of claim 16, wherein said error-list instructions comprise: atleast one error-list instruction for identifying and correcting an errorin said wellsite data by comparing a first wellsite measurement value toa second wellsite measurement value.
 18. The system of claim 17, whereinsaid first wellsite measurement value was obtained from a differentsource than said second wellsite measurement value.
 19. The system ofclaim 11, wherein said set of instructions that cause said processor toproduce said report further cause said processor to: select a template,wherein said template specifies one or more pass-fail criteria;determine a result by evaluating said wellsite data using said pass-failcriteria; and record said result in said report.
 20. The system of claim11, wherein said report is based on wellsite data from a plurality ofwellsites.